Canada

Words in human languages cluster together in phonological neighborhoods more closely than would be expected by chance. But why? One explanation is that large neighborhoods are directly selected for, possibly because they scaffold word learning and production. But it&#39;s also possible that they emerge as a byproduct of other constraints or selection pressures operating over real lexica. We advance one such selection pressure as a candidate explanation. A pressure to avoid overloading unique wordforms with homophones may lead to clusters of words that are not identical but similar. Using simulated baselines, we test the viability of this alternative account. We find that a pressure against loading too many meanings on unique wordforms––paired with the phonotactics of a target language––produces lexica with neighborhoods that are at least as large on average as those in real lexica. This does not rule out the possibility of a pro-neighborhood pressure, but it does demonstrate the viability of a parsimonious alternative account based on a pressure against homonymy for which there is independent evidence.

CogSci 2022

Can a pressure against homophones explain phonological neighborhoods?

Words in human languages cluster together in phonological neighborhoods more closely than would be expected by chance. But why? One explanation is that large neighborhoods are directly selected for, possibly because they scaffold word learning and production. But it's also possible that they emerge as a byproduct of other constraints or selection pressures operating over real lexica. We advance one such selection pressure as a candidate explanation. A pressure to avoid overloading unique wordforms with homophones may lead to clusters of words that are not identical but similar. Using simulated baselines, we test the viability of this alternative account. We find that a pressure against loading too many meanings on unique wordforms––paired with the phonotactics of a target language––produces lexica with neighborhoods that are at least as large on average as those in real lexica. This does not rule out the possibility of a pro-neighborhood pressure, but it does demonstrate the viability of a parsimonious alternative account based on a pressure against homonymy for which there is independent evidence.

technical paper

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
 [**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)

This conference requires a registration to attend. Please use the link below to secure your ticket. The website will be available to all registered attendees on July 25th. 

[Register to Attend](https://cognitivesciencesociety.org/registration/)

Registration Is Required

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Recent experimental and computational modelling work has found that languages are shaped by the referential context in which they operate. Wray and Grace (2007) argue that even compositionality, traditionally regarded as a universal and fundamental feature of human languages, may have only culturally evolved in response to changing social contexts. But how can the referential contexts of individual interactions come to shape the level of compositionality in the language of an entire community? To explore this question, we propose an iterated hierarchical Bayesian model that shows how partner-specific linguistic innovations can be generalized as community-wide features via a context-sensitive pathway. Our simulations show that the degree of compositionality that evolves in the language of a community depends on the communicative needs of its members, but also on the degree of user uncertainty over the nature of those needs, and on the level of heterogeneity in the community's needs.

The complexity of a language is shaped by the communicative needs of its users and by the hierarchical nature of their social inferences

Typological data shows a tendency for languages to exhibit harmonic (i.e. consistent) ordering between heads and dependents. However, some categories seem to contradict this tendency. Here we investigate one such case, the order of the noun with respect to two dependents—adjectives, which tend to follow the noun and genitives which precede. We report two silent gesture experiments examining (i) whether there are cognitive biases favouring postnominal adjective and prenominal genitive order in a single trial judgement task, and (ii) if those preferences continue to influence order when participants learn a complete word order system. Our results shed light on how biases for individual categories of elements interact with biases that affect the wider linguistic system. While participants strongly prefer postnominal adjectives and prenominal genitives when these are judged in isolation, when they learn a system of ordering, these biases are obscured and (at least in some cases) harmony emerges.

The Influence of Category-specific and System-wide Preferences on Cross-Linguistic Word Order Patterns

In an experimental study, we investigated how social interaction dynamics affect collective creativity. Pairs of participants collaborated in a computer game, creating “beautiful and interesting” shapes by moving tiles on a large touchscreen. We identified naturally emerging interaction styles by applying k-means clustering on participants’ tile moves. The game allowed us to quantify the unfolding creative process in a well-defined search space. Pairs characterized by a single dominating member tended to visit fewer areas of the solution space, stay there longer and created on average more (but less original) shapes. In contrast, pairs that took turns with every tile move tended to explore more, stayed in each area of the solution space for less time and created fewer (but more original) shapes. While previous literature found conflicting effects of ‘creating with another’, the current paper suggests naturally emerging interaction styles as a differentiating factor underlying how collective creativity unfolds.

Social Interaction Dynamics Modulates Collective Creativity

Studies have found that when innovation involves recombining cultural traits, partially-connected populations produce higher levels of cultural complexity than fully-connected populations by avoiding cultural homogenization. However, population connectedness is only one of many factors that could promote cultural diversity and thus cultural complexity. Here, we examine whether people's preference for copying members of their own social group could also fill this role. Our simulations reveal that even in fully-connected populations, ingroup-biased transmission results in greater cultural complexity than unbiased transmission. Moreover, in partially-connected populations, this bias interacts with population structure to produce even higher levels of cultural complexity than population structure alone. Finally, by incorporating population turnover into our model, we shed light on the trade-off between promoting cultural diversity versus limiting cultural loss.

Ingroup-Biased Copying Promotes Cultural Diversity and Complexity

Despite progress in understanding the sources of difficulty in solving insight problems, how new ideas are discovered, implemented, and learned is poorly understood. We report an experiment testing a theory of how individuals use failed attempts to discover new ideas. We compared performance on the nine-dot problem with a variant requiring solution using three lines rather than four. Results supported predictions that the three-line variant is easier than the four-line, and that transfer of solution knowledge from the three- to the four-line version is facilitative, but not vice-versa. Additionally, varying the spacing between dots facilitated discovery and transfer of solutions in both variants. Our theory specifies a priority order for seeking new ideas that offers a partial solution to the frame problem. Individuals first seek ideas from the problem statement and attempts they make. Only when these sources fail do they resort to searching memory or the external task environment.

Conceptual recoding of new ideas during and aer solution of an insight problem

The opportunistic assimilation hypothesis posits that struggling and failing to solve a problem creates failure indexes, or long-term memory traces of the problem, that creates sensitivity to environment hints that trigger insight experiences. Past laboratory research has cast doubt on the usefulness of such hints during incubation breaks, but laboratory work is limited in its ability to recreate the diversity of stimuli in everyday life the opportunistic assimilation hypothesis requires. The current paper evaluates the insight experiences of over 150 participants who solved an insight math puzzle outside the lab for the presence of incidental hints that aided with problem solving. Across two studies, participants reported that chance hints in the wild had helped them to solve the puzzle and triggered insight moments. This suggests that opportunistic assimilation may play a role in everyday insight experiences and should not be discounted in future research.

Diversity in Mathematical Insight Experiences in the Wild: Evidence of Opportunistic Assimilation

Where do mathematical insights come from? According to classic accounts, creativity is a multi-stage process that involves combining ideas in novel ways. Evidence for these accounts, however, is drawn from artificial lab-based settings or is zoomed out from the messy, moment-to-moment details of discovery. Here, we examine a video corpus of expert mathematicians generating proofs in an ecologically valid setting. We find that mathematicians begin by creating a variety of inscriptions. They then interact with these inscriptions through gaze, speech, gesture, and writing. When they experience an insight, however, their interactions become unpredictable, and they begin to connect inscriptions in novel ways (quantified by an information-theoretic measure, surprisal). Expert mathematical discovery, we conclude, exhibits the stages and combinatorial processing that have been proposed to characterize creativity. Even at the pinnacle of abstraction, at the highest levels of expertise, new ideas are born when the body discovers unexpected affinities among ideas.

Mathematical insights as novel connections: Evidence from expert mathematicians

Traditional laboratory studies have found that people are more likely to retrieve surface matches than distant analogs, suggesting that superficial similarities exert a stronger influence than structural similarities on retrieval. However, it has been contended that the observed supremacy of surface similarity may have originated in experimental conditions that are unfairly adverse for the retrieval of distant analogs, as well as in a faulty separation between surface and structural similarity during the construction of surface matches. In two experiments, we presented a target item that maintained only superficial similarities with one extra-experimental source and only structural similarities with another one. By using natural items, we were able to avoid the shallow processing often attributed to experimental analogs, while carefully controlling that surface matches did not maintain structural similarities. Converging with traditional results, our data showed a more frequent retrieval of surface matches than of distant analogs, indicating a supremacy of superficial similarities during retrieval.

Structural vs. Superficial Similarity During Unprompted Analogical Retrieval: Which one Exerts a Greater Force?

We see the external world as consisting not only of objects and their parts, but also of relations that hold between them. Visual analogy, which depends on similarities between relations, provides a clear example of how perception supports reasoning. Here we report an experiment in which we quantitatively measured the human ability to find analogical mappings between parts of different objects, where the objects to be compared were drawn either from the same category (e.g., images of two mammals, such as a dog and a horse), or from two dissimilar categories (e.g., a chair image mapped to a cat image). Humans showed systematic mapping patterns, but with greater variability in mapping responses when objects were drawn from dissimilar categories. We simulated the human response of analogical mapping using a computational model of mapping between 3D objects, visiPAM (visual Probabilistic Analogical Mapping). VisiPAM takes point-cloud representations of two 3D objects as inputs, and outputs the mapping between analogous parts of the two objects. VisiPAM consists of a visual module that constructs structural representations of individual objects, and a reasoning module that identifies a probabilistic mapping between parts of the two 3D objects. Model simulations not only capture the qualitative pattern of human mapping performance cross conditions, but also approach human-level reliability in solving visual analogy problems.

From Vision to Reasoning: Probabilistic Analogical Mapping Between 3D Objects

A key property of human cognition is its ability to generate novel predictions about unfamiliar situations by completing a partially-specified relation or an analogy. Here, we present a computational model capable of producing generative inferences from relations and analogs. This model, BART-Gen, operates on explicit representations of relations learned by BART (Bayesian Analogy with Relational Transformations), to achieve two related forms of generative inference: reasoning from a single relation, and reasoning from an analog. In the first form, a reasoner completes a partially-specified instance of a stated relation (e.g., robin is a type of ____). In the second, a reasoner completes a target analog based on a stated source analog (e.g., sedan:car :: robin:____). We compare the performance of BART-Gen with that of BERT, a popular model for Natural Language Processing (NLP) that is trained on sentence completion tasks and that does not rely on explicit representations of relations. Across simulations and human experiments, we show that BART-Gen produces more human-like responses for generative inferences from relations and analogs than does the NLP model. These results demonstrate the essential role of explicit relation representations in human generative reasoning.

Premium content

Downloads

Next from CogSci 2022

The complexity of a language is shaped by the communicative needs of its users and by the hierarchical nature of their social inferences

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES